U (dg @s^ddlmZddlmZddlmZmZmZddlm Z m Z ddl m Z ddlmZddlmZd d lmZmZd d lmZd d lmZmZmZd d lmZmZmZdddgZGddde j Z!Gddde j Z"ee#e!dddZ$GdddeZ%ede%j&fdej'fddddej'dee%e(ee#eeee!dddZ)d d lm*Z*e*d!e%j&j+iZ,dS)") OrderedDict)partial)AnyDictOptional)nnTensor) functional)SemanticSegmentation)_log_api_usage_once) WeightsEnumWeights)_VOC_CATEGORIES)IntermediateLayerGetterhandle_legacy_interface_ovewrite_value_param) MobileNetV3MobileNet_V3_Large_Weightsmobilenet_v3_largeLRASPP!LRASPP_MobileNet_V3_Large_Weightslraspp_mobilenet_v3_largecsLeZdZdZd ejeeeeddfdd Zee e efddd Z Z S) ra Implements a Lite R-ASPP Network for semantic segmentation from `"Searching for MobileNetV3" `_. Args: backbone (nn.Module): the network used to compute the features for the model. The backbone should return an OrderedDict[Tensor], with the key being "high" for the high level feature map and "low" for the low level feature map. low_channels (int): the number of channels of the low level features. high_channels (int): the number of channels of the high level features. num_classes (int, optional): number of output classes of the model (including the background). inter_channels (int, optional): the number of channels for intermediate computations. N)backbone low_channels high_channels num_classesinter_channelsreturncs,tt|||_t|||||_dS)N)super__init__r r LRASPPHead classifier)selfrrrrr __class__J/tmp/pip-unpacked-wheel-vx7f76es/torchvision/models/segmentation/lraspp.pyr"#s zLRASPP.__init__inputr cCsB||}||}tj||jddddd}t}||d<|S)NbilinearFsizemodeZ align_cornersout)rr$F interpolateshaper)r%r+featuresr1resultr(r(r)forward+s   zLRASPP.forward)r) __name__ __module__ __qualname____doc__rModuleintr"rrstrr7 __classcell__r(r(r&r)rscsBeZdZeeeeddfdd ZeeefedddZZ S)r#N)rrrrr c stttj||dddt|tjdd|_ttdtj||dddt |_ t||d|_ t||d|_ dS)NF)ZbiasT)Zinplace) r!r"rZ SequentialZConv2dZ BatchNorm2dZReLUcbrZAdaptiveAvgPool2dZSigmoidscalelow_classifierhigh_classifier)r%rrrrr&r(r)r"7s  zLRASPPHead.__init__r*cCs\|d}|d}||}||}||}tj||jddddd}||||S)Nlowhighr,r-Fr.)rArBr2r3r4rCrD)r%r+rErFxsr(r(r)r7Fs  zLRASPPHead.forward) r8r9r:r=r"rr>rr7r?r(r(r&r)r#6sr#)rrr cCsz|j}dgddt|Dt|dg}|d}|d}||j}||j}t|t|dt|did }t||||S) NrcSs g|]\}}t|ddr|qS)Z_is_cnF)getattr).0ibr(r(r) Vs z'_lraspp_mobilenetv3..r@rErF)Z return_layers)r5 enumeratelenZ out_channelsrr>r)rrZ stage_indicesZlow_posZhigh_posrrr(r(r)_lraspp_mobilenetv3Rs&  rRc @s>eZdZedeedddeddddd d id d d ZeZdS)rzJhttps://download.pytorch.org/models/lraspp_mobilenet_v3_large-d234d4ea.pthi)Z resize_sizei"(1)r@r@z]https://github.com/pytorch/vision/tree/main/references/segmentation#lraspp_mobilenet_v3_largezCOCO-val2017-VOC-labelsg33333L@gV@)ZmiouZ pixel_accz These weights were trained on a subset of COCO, using only the 20 categories that are present in the Pascal VOC dataset. )Z num_params categoriesZmin_sizeZrecipeZ_metricsZ_docs)urlZ transformsmetaN) r8r9r:rrr rCOCO_WITH_VOC_LABELS_V1DEFAULTr(r(r(r)r`s  Z pretrainedZpretrained_backbone)weightsweights_backboneNT)rXprogressrrY)rXrZrrYkwargsr cKs|ddrtdt|}t|}|dk rJd}t|t|jd}n |dkrVd}t|dd}t ||}|dk r| |j |d |S) a|Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone from `Searching for MobileNetV3 `_ paper. .. betastatus:: segmentation module Args: weights (:class:`~torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. num_classes (int, optional): number of output classes of the model (including the background). aux_loss (bool, optional): If True, it uses an auxiliary loss. weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained weights for the backbone. **kwargs: parameters passed to the ``torchvision.models.segmentation.LRASPP`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights :members: Zaux_lossFz&This model does not use auxiliary lossNrST)rXZdilated)rZ) popNotImplementedErrorrverifyrrrQrUrrRZload_state_dictZget_state_dict)rXrZrrYr[rmodelr(r(r)rxs%     ) _ModelURLsZlraspp_mobilenet_v3_large_coco)- collectionsr functoolsrtypingrrrZtorchrrZtorch.nnr r2Ztransforms._presetsr utilsr Z_apirrZ_metar_utilsrrrZ mobilenetv3rrr__all__r<rr#r=rRrrVZ IMAGENET1K_V1boolrrarTZ model_urlsr(r(r(r)sJ       #7